Name
Title | ||
|---|---|---|
Application of adaptive wavelet neural network to forecast operating reserve requirements in forward ancillary services market |
Abstract | ||
|---|---|---|
Operating reserve (OR) is a major portion of ancillary services (AS) in a competitive electricity market and need to be procured by independent system operator (ISO), to achieve a high degree of power system reliability and security, following the major generation and transmission contingencies. Several ISOs have adopted deterministic methods to assess the OR requirements, however, such methods do not explicitly consider the unforeseen load swings and the probability of equipment outages. This paper proposes an adaptive wavelet neural network (AWNN) based two-stage approach to forecast OR requirements for both day-ahead and hour-ahead AS market in the California ISO (CAISO) controlled grid. The AWNN is a new class of feed-forward neural network with continuous wavelet function as the hidden layer node's activation function. The forecasting results for winter and summer seasons of the year 2007 are presented and compared with those obtained by feed-forward multi-layer perceptron neural network (MLPNN). It is found that AWNN based proposed method outperforms the MLPNN model. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.asoc.2010.05.026 | Appl. Soft Comput. |
Keywords | Field | DocType |
independent system operator,adaptive wavelet neural network,mlpnn model,major portion,operating reserve requirements forecasting,competitive electricity market,feed-forward neural network,california iso,ancillary services market,activation function,reserve requirement,continuous wavelet function,feed-forward multi-layer perceptron neural network,major generation,multi layer perceptron,feed forward neural network,neural network,feed forward,seasonality | Electricity market,Mathematical optimization,Operating reserve,Activation function,Continuous wavelet,Electric power system,Artificial intelligence,Artificial neural network,Perceptron,Mathematics,Machine learning,Grid | Journal |
Volume | Issue | ISSN |
11 | 2 | Applied Soft Computing Journal |
Citations | PageRank | References |
2 | 0.37 | 1 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Naran M. Pindoriya | 1 | 16 | 3.86 |
| S. N. Singh | 2 | 99 | 13.38 |
| S. K. Singh | 3 | 35 | 6.95 |